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In contrast to engineering applications, in which the structure of control laws are designed to satisfy prescribed function requirements, in biology it is often necessary to infer gene-circuit function from incomplete data on gene-circuit structure. By using the feed-forward loop as a model system, this paper introduces a technique for classifying gene-circuit function given gene-circuit structure. In simulations performed on a comprehensive set of models that span a broad range of parameter space, some designs are robust, producing one unique type of functional response regardless of parameter selection. Other designs may exhibit a variety of functional responses, depending upon parameter values. We conclude that, although some feed-forward loop models have designs that lend themselves to unique function inference, others have designs for which the function type may be uncertain.
We assess the impact of cell cycle noise on gene circuit dynamics. For bistable genetic switches and excitable circuits, we find that transitions between metastable states most likely occur just after cell division and that this concentration effect
High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions,
Inferring functional relationships within complex networks from static snapshots of a subset of variables is a ubiquitous problem in science. For example, a key challenge of systems biology is to translate cellular heterogeneity data obtained from si
The current pandemic of SARS-CoV-2 has caused extensive damage to society. The characterization of SARS-CoV-2 profiles has been addressed by researchers globally with the aim of resolving this disruptive crisis. This investigation process is indispen
There is growing appreciation that gene function is connected to the dynamic structure of the chromosome. Here we explore the interplay between three-dimensional structure and transcriptional activity at the single cell level. We show that inactive l